Critical Review, Development, and Testing of a Taxonomy for Adverse Events and Near Misses in the Emergency Department

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage

BACKGROUND

Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of patient safety classifications and ontologies.

METHODS

We searched different databases and ontology/classification repositories to identify these classifications and ontologies. We included patient safety-related taxonomies, ontologies, classifications, and terminologies. We identified and extracted different concepts covered by these systems and mapped these concepts to international classification for patient safety (ICPS) and finally compared the content of these systems.

RESULTS

Finally, 89 papers (77 classifications or ontologies) were analyzed. Thirteen classifications have been developed to cover all medical domains. Among specific domain systems, most systems cover medication (16), surgery (8), medical devices (3), general practice (3), and primary care (3). The most common patient safety-related concepts covered in these systems include incident types (41), contributing factors/hazards (31), patient outcomes (29), degree of harm (25), and action (18). However, stage/phase (6), incident characteristics (5), detection (5), people involved (5), organizational outcomes (4), error type (4), and care setting (3) are some of the less covered concepts in these classifications/ontologies.

CONCLUSION

Among general systems, ICPS, World Health Organization's Adverse Reaction Terminology (WHO-ART), and Ontology of Adverse Events (OAE) cover most patient safety concepts and can be used as a gold standard for all medical domains. As a result, reporting systems could make use of these broad classifications, but the majority of their covered concepts are related to patient outcomes, with the exception of ICPS, which covers other patient safety concepts. However, the ICPS does not cover specialized domain concepts. For specific medical domains, MedDRA, NCC MERP, OPAE, ADRO, PPST, OCCME, TRTE, TSAHI, and PSIC-PC provide the broadest coverage of concepts. Many of the patient safety classifications and ontologies are not formally registered or available as formal classification/ontology in ontology repositories such as BioPortal. This study may be used as a guide for choosing appropriate classifications for various applications or expanding less developed patient safety classifications/ontologies. Furthermore, the same concepts are not represented by the same terms; therefore, the current study could be used to guide a harmonization process for existing or future patient safety classifications/ontologies.

Framing healthcare professionals in written adverse events: A discourse analysis

Healthcare professionals have a major responsibility to protect patients from harm. Despite vast efforts to decrease the number of adverse events, the progression of patient safety has internationally been acknowledged as slow. From a social construction perspective, it has been argued that the understanding of patient safety is contextual based on historical and structural rules, and that this meaning construction points out different directions of possible patient safety actions. By focusing on fact construction and its productive and limiting effect on how something can be understood, we explored the discourses about healthcare professionals in 29 written reports of adverse events as reported by patients, relatives, and healthcare professionals. Through the analysis, a discourse about the healthcare professionals as experts was found. The expert role most dominantly included an understanding that adverse events were identified through physical signs and that patient safety could be prevented by more strictly following routines and work procedures. We drew upon the conclusion that these regimes of truth brought power to the expert discourse, to the point that it became difficult for patients and relatives to engage in patient safety actions on their terms.

The Emergency Department Trigger Tool: Validation and Testing to Optimize Yield

OBJECTIVE

Recognized as a premier approach for adverse event (AE) detection, trigger tools have been developed for multiple clinical settings outside the emergency department (ED). We recently derived and tested an ED trigger tool (EDTT) with enhanced features for high-yield detection of harm, consisting of 30 triggers associated with AEs. In this study, we validate the EDTT in an independent sample and compare record selection approaches to optimize yield for quality improvement.

METHODS

This is a retrospective observational study using data from 13 months of visits to an urban, academic ED by patients aged ≥ 18 years (92,859 records). We conducted standard two-tiered trigger tool reviews on an independent validation sample of 3,724 records with at least one of the 30 triggers found associated with AEs in our previous derivation sample (N = 1,786). We also tested three new candidate triggers and reviewed 72 records with no triggers for comparison purposes. We compare derivation and validation samples on: 1) triggers showing persistent associations with AEs, 2) AE yield (AEs detected/records reviewed), and 3) representativeness of AE types detected. We use bivariate associations of triggers with AEs as the basis for trigger selection. We then use multivariable modeling in the combined derivation and validation samples to determine AE risk scores using trigger weights. This allows us to predict occurrence of AEs and derive population prevalence estimates. Finally, we compare yield for detection of AEs under three record selection strategies (random selection, trigger counts, weighted trigger counts).

RESULTS

Twenty-four of the 30 triggers were confirmed to be associated with AEs on bivariate testing. Three previously marginal triggers and two of three new candidate triggers were also found to be associated with AEs. The presence of any of these 29 triggers was associated with an AE rate of 10% in our selected sample (compared to 1.1% for none, p < 0.001). The risk of an AE increased with number of triggers. Combining data from both phases, we identified 461 AEs in 429 unique visits in 5,582 records reviewed. Our multivariable model (which emphasized parsimony) retained 12 triggers with a ROC AUC of 82% in both samples. Selecting records for review based on number of triggers improves yield to 14% for 4+ triggers (top 10% of visits) and to 28% for 8+ (top 1%). A weighted trigger count has corresponding yields of 18 and 38%. The method for selecting records for review did not appear to affect event-type representativeness, with similar distributions of event types and severities detected.

CONCLUSIONS

In this single-site study of the EDTT we observed high levels of validity in trigger selection, yield, and representativeness of AEs, with yields that are superior to estimates for traditional approaches to AE detection. Record selection using weighted triggers outperforms a trigger count threshold approach and far outperforms random sampling from records with at least one trigger. The EDTT is a promising efficient and high-yield approach for detecting all-cause harm to guide quality improvement efforts in the ED.

The Emergency Department Trigger Tool: A Novel Approach to Screening for Quality and Safety Events

STUDY OBJECTIVE

Trigger tools improve surveillance for harm by focusing reviews on records with "triggers" whose presence increases the likelihood of an adverse event. We refine and automate a previously developed emergency department (ED) trigger tool and present record selection strategies to further optimize yield.

METHODS

We specified 97 triggers for extraction from our electronic medical record, identifying 76,894 ED visits with greater than or equal to 1 trigger. We reviewed 1,726 records with greater than or equal to 1 trigger, following a standard trigger tool review process. We validated query performance against manual review and evaluated individual triggers, retaining only those associated with adverse events in the ED. We explored 2 approaches to enhance record selection: on number of triggers present and using trigger weights derived with least absolute shrinkage and selection operator logistic regression.

RESULTS

The automated query performed well compared with manual review (sensitivity >70% for 80 triggers; specificity >92% for all). Review yielded 374 adverse events (21.6 adverse events per 100 records). Thirty triggers were associated with risk of harm in the ED. An estimated 10.3% of records with greater than 1 of these triggers would include an adverse event in the ED. Selecting only records with greater than or equal to 4 or greater than or equal to 9 triggers improves yield to 17% and 34.8%, respectively, whereas use of least absolute shrinkage and selection operator trigger weighting enhances the yield to as high as 52%.

CONCLUSION

The ED trigger tool is a promising approach to improve yield, scope, and efficiency of review for all-cause harm in emergency medicine. Beginning with a broad set of candidate triggers, we validated a computerized query that eliminates the need for manual screening for triggers and identified a refined set of triggers associated with adverse events in the ED. Review efficiency can be further enhanced with enhanced record selection.